Overview
This course will provide an overview of key issues that affect metabolomics studies, handling datasets and procedures for the analysis of metabolomics data using bioinformatics tools. It will be delivered using a mixture of lectures, computer-based practical sessions and interactive discussions. The course will provide a platform for discussion of the key questions and challenges in the field of metabolomics, from study design to metabolite identification.
Audience
This course is aimed at PhD students, post-docs and researchers with at least one to two years of experience in the field of metabolomics who are seeking to improve their skills in metabolomics data analysis. Participants ideally must have working experience using R (including a basic understanding of the syntax and ability to manipulate objects).
Syllabus, tools and resources
During this course you will learn about:
- Metabolomics study design, workflows and sources of experimental error, difference between target and un-target approaches
- Metabolomics data processing tools: hands on open source R based programs, XCMS, MetFrag, MetFusion, rNMR, BATMAN
- Metabolomics data analysis: Using R Bioconductor, understanding usage of univariate and multivariate data analysis, data fusion concepts, data clustering and regression methods
- Metabolomics downstream analyses: KEGG, BioCyc, and MetExplore for metabolic pathway and network analysis with visualisation of differential expression, understanding metabolomics flux analysis
- Metabolomics standards and databases: data dissemination and deposition in EMBL- EBI MetaboLights repository; PHENOMenal, workflows4metabolomics
- Metabolomics Flux and Stable Isotope Resolved Metabolomics (SIRM)
Outcomes
After this course you should be able to:
- Discuss major principles of metabolomics experimental design and factors that impact upon subsequent analysis
- Identify strengths and weaknesses in a variety of metabolomics analytical approaches
- Use a range of Bioinformatics software to pre-process, process and analyse metabolomics data
- Discuss current trends and challenges in metabolomics
